Tilling the Soil in Tanzania: What Do Emerging Economies Have to Offer?
Bibliographic record
Abstract
Close to 70% of Tanzanian farmers are small scale resource-poor subsistence operators, cultivating an average of less than 1 to 3 hectares of mainly rain-fed land, deteriorated by continuous cropping and lack of fertility management. In the farmers’ effort to move up the commercialisation continuum and alleviate poverty through increased output and incomes, innovation and technical change is key. However, liquidity constraints and prohibitive prices have in the past discouraged farmer investment in capital goods (power tillers and tractors). This is a limiting factor for increased cropping area and timeliness of operation which has the potential to positively affect crop output and incomes. In the face of these difficulties, the farmer is prepared to trade-off quality and variety, for relatively low priced capital goods, provided they are good enough and rely less on heavily built infrastructure. In recent decades, the capital goods market for power tillers and tractors has become dynamic with respect to cost, quality and origin of production. With new entrants like China, India and Pakistan joining Western Europe, USA and Japan in the supply of farm machinery, the range of choice for the Tanzanian farmer is increasing. Chinese, Indian and Pakistani power tillers and tractors have some distinctiveness in their engineering, acquisition cost, operational cost and their supply chains which may be useful in more ways to the small farmer in Tanzania. This thesis appraises the pro-poor nature of emerging economy tillage capital goods, placing particular emphases on how an optimal technological choice is made. It examines the role that cost innovators’ from emerging economies (China/India/Pakistan) are playing in meeting the farmers’ choice objective particularly with regard to cost, labour intensity and scale of operation. In as far as Tanzanian farmers are concerned the study discusses the role that local institutions can play to enhance choice, access and efficient use of such capital goods for higher productivity which may translate into increased incomes. The study draws on both qualitative and quantitative data to compare advanced country tractors and power tillers with those from emerging economies and finds that; First, aid/government support, trade and FDI/licencing are key conduits for technology imports into Tanzania. However, trade has been very important for emerging economy machines whilst aid/government support has been found to be key for advanced country machines. Second, in terms of penetration and extent of use among Tanzanian farmers emerging economy machines are more popular than advanced country ones when it comes to power tillers. Nevertheless, the total stock of advanced country tractors in Tanzania are known to be larger than emerging economy ones; though we are recently witnessing a recent rapid increase in the former than the latter. Third, advanced country machines are generally superior in terms of engineering performance and work efficiency when compared with emerging economy ones. That said, it is worth noting that the advanced country machines are capital intensive and involve higher maintenance costs because of higher spare parts and repair cost. Finally, emerging economy machines are more pro-poor than matured market ones since they create more opportunities for employment and capability building among capital constrained users and dealers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".